3 papers
cs.CL2024
SelfPrompt: Autonomously Evaluating LLM Robustness via Domain-Constrained Knowledge Guidelines and Refined Adversarial Prompts
Aihua Pei, Zehua Yang, Shunan Zhu +2
Traditional methods for evaluating the robustness of large language models (LLMs) often rely on standardized benchmarks, which can escalate costs and limit evaluations across varie…
cs.AI2024
Reinforcement Learning from Multi-role Debates as Feedback for Bias Mitigation in LLMs
Ruoxi Cheng, Haoxuan Ma, Shuirong Cao +6
Bias in LLMs can harm user experience and societal outcomes. However, current bias mitigation methods often require intensive human feedback, lack transferability to other topics o…
cs.CL2024
KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge Graphs
Aihua Pei, Zehua Yang, Shunan Zhu +3
Existing frameworks for assessing robustness of large language models (LLMs) overly depend on specific benchmarks, increasing costs and failing to evaluate performance of LLMs in p…